Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography

Adams, David C.; Pahlevaninezhad, Hamid; Szabari, Margit V.; Cho, Josalyn L.; Hamilos, Daniel L.; Kesımer, Mehmet; Boucher, Richard C.; Luster, Andrew D.; Medoff, Benjamin D.; Suter, Melissa J.

doi:10.1364/boe.8.004729

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…Mucus contrast demonstrated the most significant post‐allergen difference between the AA and AC groups ( P < 0.0001). Although mucus contrast is likely influenced by factors such as cellular debris, we have already demonstrated that mucus contrast correlates with mucin content obtained from BAL . Although further tests are necessary to establish correlations, this metric seems to accurately capture the appearance of mucus in our images (refer to the more solid appearing mucus in the AA airways of Figs ).…”

Section: Discussionmentioning

confidence: 71%

“…OCT images were processed according to standard processing techniques . Four distinct metrics were quantified per data set: epithelial thickness, mucosal thickness, mucosal buckling (as a measure of bronchoconstriction) and mucus contrast (an optical assessment of mucus content). Analysis of all excluding mucus contrast was performed using ImageJ (National Institute of Health, Bethesda, MD, USA).…”

Section: Methodsmentioning

confidence: 99%

“…Mucus (Fig. D) was segmented and assessed automatically . The final values are reported as the average of each of the measurements obtained per segment.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative assessment of airway remodelling and response to allergen in asthma

et al. 2019

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Background and objective: In vivo evaluation of the microstructural differences between asthmatic and nonasthmatic airways and their functional consequences is relevant to understanding and, potentially, treating asthma. In this study, we use endobronchial optical coherence tomography to investigate how allergic airways with asthma differ from allergic non-asthmatic airways in baseline microstructure and in response to allergen challenge. Methods: A total of 45 subjects completed the study, including 20 allergic, mildly asthmatic individuals, 22 non-asthmatic allergic controls and 3 healthy controls. A 3-cm airway segment in the right middle and right upper lobe were imaged in each subject immediately before and 24 h following segmental allergen challenge to the right middle lobe. Relationships between optical airway measurements (epithelial and mucosal thicknesses, mucosal buckling and mucus) and airway obstruction (FEV 1 /FVC (forced expiratory volume in 1 s/forced vital capacity) and FEV 1 % (FEV 1 as a percentage of predictive value)) were investigated. Results: Significant increases at baseline and in response to allergen were observed for all four of our imaging metrics in the asthmatic airways compared to the non-asthmatic airways. Epithelial thickness and mucosal buckling exhibited a significant relationship to FEV 1 /FVC in the asthmatic group. Conclusion: Simultaneous assessments of airway microstructure, buckling and mucus revealed both structural and functional differences between the mildly asthmatic and control groups, with airway buckling seeming to be the most relevant factor. The results of this study demonstrate that a comprehensive, microstructural approach to assessing the airways may be important in future asthma studies as well as in the monitoring and treatment of asthma. † P < 0.05 for post hoc comparison with AC. ‡ P < 0.05 for post hoc comparison with HC. § HC group excluded and Mann-Whitney test was used for statistical analysis. AA, allergic asthmatic; AC, allergic control; BAU, bioequivalent allergen unit; FEV 1 , forced expiratory volume in 1 s; FVC, forced vital capacity; HC, healthy control; PC20, concentration of methacholine needed to produce a 20% fall of FEV 1 from baseline.

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Section: Discussionmentioning

confidence: 71%

Section: Methodsmentioning

confidence: 99%

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Quantitative assessment of airway remodelling and response to allergen in asthma

et al. 2019

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“…In the clinical setting, a simple tissue surface identification algorithm would be sufficient. Our laboratory has already published automated methods for tissue surface identification, which could easily be applied to this setting (22,49).…”

Section: Discussionmentioning

confidence: 99%

Distinguishing Tumor from Associated Fibrosis to Increase Diagnostic Biopsy Yield with Polarization-Sensitive Optical Coherence Tomography

Hariri

Adams

Applegate

et al. 2019

Clinical Cancer Research

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Purpose: With recent advancements in personalized medicine, biopsies must contain sufficient tumor for histologic diagnosis and molecular testing. However, inadvertent biopsy of tumor-associated fibrosis compromises tumor yield, resulting in delayed diagnoses and/or repeat procedures when additional tumor is needed. The ability to differentiate tumor from fibrosis intraprocedurally during biopsy could significantly increase tumor yield. Polarization-sensitive optical coherence tomography (PS-OCT) is an imaging modality that is endoscope-and/or needle-compatible, and provides large volumetric views of tissue microstructure with high resolution ($10 mm) while simultaneously measuring birefringence of organized tissues such as collagen. We aim to determine whether PS-OCT can accurately detect and distinguish tumor-associated fibrosis from tumor. Experimental Design: PS-OCT was obtained ex vivo in 64 lung nodule samples. PS-OCT birefringence was measured and correlated to collagen content in precisely matched histology, quantified on picrosirius red (PSR) staining. Results: There was a strong positive correlation between PS-OCT measurement of birefringent fibrosis and total collagen content by PSR (r ¼ 0.793; P < 0.001). In addition, PS-OCT was able to accurately classify tumor regions with >20% fibrosis from those with low fibrosis (20%) that would likely yield higher tumor content (P < 0.0001). Conclusions: PS-OCT enables accurate fibrosis detection and can distinguish tumor regions with low fibrosis. PS-OCT has significant potential for clinical impact, as the ability to differentiate tumor from fibrosis could be used to guide intraprocedural tissue sampling in vivo, or for rapid biopsy adequacy assessment ex vivo, to increase diagnostic tumor yield essential for patient care and research.

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“…Each autosegmented image was evaluated using texture analysis. 7 , 27 , 28 This was done using the GLCM—a statistical method of examining the spatial distributions of pixels. Four different tissue texture properties (correlation, homogeneity, contrast, and energy) were analyzed at four different angles of the GLCM (0 deg, 45 deg, 90 deg, and 135 deg), resulting in 16 unique texture variables for each image.…”

Section: Methodsmentioning

confidence: 99%

Dynamic programming and automated segmentation of optical coherence tomography images of the neonatal subglottis: enabling efficient diagnostics to manage subglottic stenosis

et al. 2019

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. Subglottic stenosis (SGS) is a challenging disease to diagnose in neonates. Long-range optical coherence tomography (OCT) is an optical imaging modality that has been described to image the subglottis in intubated neonates. A major challenge associated with OCT imaging is the lack of an automated method for image analysis and micrometry of large volumes of data that are acquired with each airway scan (1 to 2 Gb). We developed a tissue segmentation algorithm that identifies, measures, and conducts image analysis on tissue layers within the mucosa and submucosa and compared these automated tissue measurements with manual tracings. We noted small but statistically significant differences in thickness measurements of the mucosa and submucosa layers in the larynx ( ), subglottis ( ), and trachea ( ). The automated algorithm was also shown to be over 8 times faster than the manual approach. Moderate Pearson correlations were found between different tissue texture parameters and the patient’s gestational age at birth, age in days, duration of intubation, and differences with age (mean age 17 days). Automated OCT data analysis is necessary in the diagnosis and monitoring of SGS, as it can provide vital information about the airway in real time and aid clinicians in making management decisions for intubated neonates.

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Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography

Cited by 12 publications

References 30 publications

Quantitative assessment of airway remodelling and response to allergen in asthma

Quantitative assessment of airway remodelling and response to allergen in asthma

Distinguishing Tumor from Associated Fibrosis to Increase Diagnostic Biopsy Yield with Polarization-Sensitive Optical Coherence Tomography

Dynamic programming and automated segmentation of optical coherence tomography images of the neonatal subglottis: enabling efficient diagnostics to manage subglottic stenosis

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